🌐 AgentGrid: Open Agentic Web

AgentGrid is the third generation of agent architectures (Gen-3).
It subsumes strengths of Gen-1: AI Agents (autonomous intelligent entities) and Gen-2: Multi-Agent Systems (MAS) (distributed problem solvers), while overcoming their limits.

Where Gen-1 solves as lone agent and Gen-2 organizes coordinated teams, AgentGrid operationalizes a decentralized society of agents like the Internet of agents - billions of interconnected agents that can discover each other, negotiate, form ad-hoc collaborations, exchange knowledge, and coordinate actions across open environments.

Key Idea: AgentGrid is foundational infrastructure for the Open Agentic Web - enabling agents to pursue individual and collective goals under shared norms, policies, and guarantees. It transforms isolated agents into participants of a large, cooperative, and dynamic ecosystem that spans trust, adversarial settings, and large-scale distributed problem solving.

🧩 A Simple Analogy

Gen-1 (AI Agent): Like a skilled individual worker - they can perceive, decide, act, and learn, but they work mostly alone.
Gen-2 (MAS): Like a project team - several workers with different roles (planner, doer, verifier) coordinating through set rules to solve a shared problem.
Gen-3 (AgentGrid): Like a Civilization - billions of people, organizations, and services interacting freely: forming organizations, coalitions, negotiating contracts, delegating tasks, sharing knowledge, competing and collaborating at scale. No central control, yet society functions through shared norms, infrastructure, and governance.

AgentGrid is this Civilization for AI agents - a digital society where diverse agents can meet, trust, collaborate, and evolve collective intelligence.

🌍 AgentGrid as a Digital Civilization

AgentGrid does everything a single agent can do: be goal driven, perceive its environment, reason, plan, decide, act through tools, and learn from feedback to pursue goals under constraints.

It also provides everything a Multi-Agent System (MAS) can do:
- Coordinate autonomous agents to achieve outcomes no single agent can.
- Solve distributed problems where each agent has incomplete information, no global control, and decentralized data.
- Organize agents into roles (planner, doer, verifier, broker), exchanging intents, proposals, and receipts.
- Leverage composition, workflows and orchestration. - Collaborate by uniform protocols - Use LLMs as agent “brains,” and tools for actuation.

But AgentGrid goes beyond both. It delivers what an open environment - a true society of agents or Internet of Agents demands:

🌐 What AgentGrid Delivers

AgentGrid integrates with several key projects, each contributing a unique piece of the Open Agentic Web:

Capability	Brief Description
🔍 Discovery	Agents must find and recognize each other across vast, dynamic networks.
📡 Communication Systems	Decentralized communication mesh with diverse channels and messaging for rich, asynchronous, large-scale interaction.
📜 Open Protocols	Flexible, interoperable standards for any transaction — no enforced uniformity, since billion-scale ecosystems cannot rely on a single format.
🔐 Trust & Identity	Verifiable identity, reputation, and guarantees even in adversarial conditions.
💱 Economy & Exchange	Marketplaces, task exchanges, and resource-sharing infrastructures for trade, pricing, and value transfer among agents.
🤝 Negotiation & Contracts	Mechanisms to form, validate, and enforce agreements at scale.
🏛️ Collective Governance	Norms, institutions, and policy frameworks that balance autonomy with shared order.
🌍 Civilizational Systems	Meta-structures (law, culture, governance) that sustain large-scale cooperation across heterogeneous agents.
🔗 Knowledge & Context Sharing	Seamless exchange of data, context, and insights across diverse agents and environments.
👥 Agency & Coalitions	Ability for agents to join or form agencies, roles, groups, and alliances for common or competitive goals.
🧭 Strategies Decision & Behavior	Adaptive mechanisms for deliberation, decision, competition, cooperation, or divergence in complex settings.
⚖️ Scalability	Billions of heterogeneous agents interacting without central control.

🏗️ Core Building Blocks of AgentGrid

The AgentGrid is not a single system but a constellation of key projects that together form the foundation of the Open Agentic Web.

The AgentGrid is built upon the following key projects, each contributing a unique piece of the Open Agentic Web:

Project	Intuitive Brief
🤖 AIOS	Operating system for AI & agents; runtime, orchestration, and execution environment.
🛡️ PolicyGrid	Trust and governance layer; aligns AI & agents with shared norms, ethics, and rules.
🎮 OpenArcade	Framework to shape agent populations; enables strategies for interaction, collaboration, cooperation, negotiation, and social decision-making.
🔌 ServiceGrid	Service, tool discovery and composition; connects agents to distributed services & tools.
🔐 Xchange.id	Decentralized task exchange for agents & AI; routes tasks to specialist agents or agencies.
🌐 OpenMe.sh	Open, protocol-native communication mesh; enables signaling, message exchange, and shared context across groups, orgs, and geographies.
📜 ContractGr.id	Contracts and agreements for AI-first society; formalizes negotiation, commitments, and enforcement.
🔗 Pervasive.Link	Meta-protocol that binds heterogeneous systems; encodes, translates protocols, context, languages, and strategies into interoperable structures.
🚉 OpenHub.ai	Market hub for decentralized intelligence; backbone for sourcing, distribution, and routing of networked intelligence.
🏛️ AgencyGr.id	Societal layer; defines roles, structures, and institutions for collective organization.

AI Agent (Gen-1)

AI Agent is an intelligent entity with degree of autonomy. AI agent perceives an environment, reasons, plans and decides what to do, acts through tools, and learns or updates state from feedback to pursue goals under constraints - all while guided by policies.
They have control both over their own internal state and over their behavior.

Core primitives of Agent: - Observations: Inputs from apps, APIs, sensors, or text. - Policy: The decision function (often an LLM + rules) that maps observations→actions. - Memory: Episodic (recent context) and semantic (long-term), with provenance & retrieval policies. - Tools/Actuators: APIs, code execution, function call, databases, robots - anything that changes the environment of operaton. - Goals & Constraints: Objectives, budgets, deadlines, and a safety envelope (permissions/policy). - Loop: Perception→World Model update ↔ Planning ↔ Action ↔ Reflection.

A Gen-1 Agent is a self-contained perception–plan–act loop with local memory and tool use within one trust domain. It excels at well-scoped tasks, deterministic pipelines, and quick iteration.

When to use: Focused workflows; a single user or team; limited tools; low coordination needs; short-lived tasks.

Limits: brittle generalization, single point of failure, narrow tool surface, and no native notion of coordination or trustbeyond its host environment.

Multi Agent System (Gen-2)

A multi-agent system is a collection of autonomous agents that pursue goals in a distributed problem, and coordinate via protocols to deliver system-level outcomes that no single agent can.
They are mostly used to solve problems that are beyond the individual capabilities or knowledge of each individual.

MAS can be seen as distributed problem solvers (DPS) where each agent has incomplete information or capabilities for solving the problem and, thus, has a limited viewpoint; there is no system global control; data are decentralized; and computation is asynchronous.

In MAS, each agent owns a role (planner, doer, verifier, broker), speaks a protocol (intents, proposals, receipts), and trades off speed/quality/cost under policy-as-code governance.
MAS uses LLMs as agent “brains,” tools for actuation, and policies for control.

Interactions in MAS can be either cooperative or selfish. That is, the agents can share a common goal (e.g., an ant colony), or they can pursue their own interests (as in a free market economy).
In cooperative situations, agents collaborate to achieve a common goal, shared between the agents or, alternatively, the goal of a central designer who is designing the various agents.
Interaction between selfish agents usually uses coordination techniques based on auctions or other resource sharing mechanisms.

The power comes from composition (many simple parts), orchestration, parallelism, redundancy, and role diversity.

MAS-Gen2 as Orchestration

In orchestration, there’s a predefined script or plan and a conductor - not necessarily a literal central controller, but a design-time imposition of: - Fixed agent roles and capabilities - Predefined communication protocols - Known environment parameters - Goal-aligned reward structures

The global behavior reflects the designer’s intent more than the agents’ spontaneous decisions.

Even if the system is distributed at runtime, the behavioral envelope is set in advance.
Agents interact within designer-defined constraints, and “emergent” behavior often means unexpected combinations of expected primitives, not truly novel dynamics.

For MAS, Choreography is rare & difficult because: - Design Control Bias: Most MAS are built for predictable outcomes, not surprises. - Limited Diversity: Agent designs share architecture, language, and objectives. - Static Protocols: Interaction schemas rarely evolve at runtime. - No Open-World Dynamics: External unknowns and new agent types are excluded.

Core primitives of MAS:

Coordination (Scripted): Preselected plan & Orchestration.
Protocols (Fixed and Version-Controlled): Interaction rules are locked to specific, approved versions, ensuring all agents follow identical formats. Any changes require formal updates and redeployment; no runtime protocol evolution or hybridization.
Topologies (Fixed/Enumerated): Known membership, addressing, and routing graphs. Discovery via registry or config; join/leave events are controlled, not open.
Trust & Identity (Closed-Set): Whitelists, RBAC, and managed PKI within a controlled boundary. Reputation (if any) is scoped and static; no open admission of unknown agents.
Predefined Agent Roles: Fixed functional identities set at design time for capability stability. Enforces predictable task boundaries; no spontaneous role invention.
Limited Role Interchangeability: Agents stay within their type’s capability envelope. Cross-role adoption requires explicit redesign, not runtime drift.
Bounded Environment Model: Finite state/action space known before deployment. No significant external surprises; simulatable dynamics and closed-world guarantees.
Designer-Defined Objectives: Goals/rewards embedded and not self-modifiable. Ensures alignment with intended outcomes; limits adaptive re-goaling.
Deterministic Execution Pathways: Repeatable decision flows under identical inputs. Supports verification, testing, and auditability.

Limits

Assumes single-organization trust, private buses, homogeneous policy. It lacks agency, fluidity, identity, reputation, and sovereignty concepts needed when agents cross company, product, or jurisdictional boundaries.
MAS frameworks often rely on hard-coded communication pipelines or predefined protocols, limiting their adaptability to dynamic task requirements.
Gen-2 MAS frameworks only consider agents defined within their own ecosystems, potentially blocking the integration of various third-party agents and limiting the diversity of agent capabilities and the platform’s generality.
Face challenges in distributed environments, as most frameworks are limited to single-device setups.
Emergence in the strict sense, where novel, unpredictable global behaviors arise from local interactions, is often constrained or absent in traditional Multi-Agent Systems (MAS) because MAS are typically:
- Closed-world: Agent types, rules, and environments are predefined.
- Finite-state: Interaction protocols and outcomes are bounded by design.
- Goal-bounded: Optimization is constrained to system-specified objectives.
In second-generation multi-agent systems (Gen-2 MAS), the lack of explicit, well-defined agency has been a key barrier to operating effectively in open environments such as agent societies or the Internet of Agents.
- Many Gen-2 architectures focused heavily on agent capabilities, communication protocols, and task execution, but often left agency - the formal structures of roles, responsibilities, authority, and interaction rules - implicit / internal to agent or underdeveloped.
- In closed or highly controlled environments, this omission could be masked by fixed system parameters. But in open, heterogeneous ecosystems where agents come and go, interact across trust boundaries, and pursue diverse goals, the absence of explicit agency structures leads to fragility, misalignment, and coordination breakdowns.
- Without agency, agents cannot reliably establish shared objectives, resolve conflicts, verify roles, or maintain consistent governance - all critical for long-term stability and large-scale cooperation.
- Addressing this gap is essential for enabling self-organizing, resilient, and pluralistic agent societies capable of functioning within the dynamic, unpredictable landscapes of the Internet of Agents.
These constraints make MAS more engineered ecosystems than living societies, limiting their capacity for genuine novelty.

When to use

Controlled Coordination: Executing tasks in predictable ways with distributed but well-aligned agents.
Simulation: Modeling swarm behavior, traffic flows, market dynamics under controlled conditions.
Closed-Loop Distributed Problem Solving: Excels at executing bounded sensing–decision–action cycles with predictable convergence and verifiable outcomes.
Decomposition into roles: Partition complex tasks into predefined & static specialized agent roles with stable interfaces.
Parallelism: Scale workload across many homogeneous agent instances in parallel, ensuring high throughput and consistent task completion.
Cross-skill pipelines within org: Supports heterogeneous but fixed, predefined agent pipelines where each stage adds domain-specific value in a controlled sequence.
Resource-Bound Optimization: Provides strict orchestration needed for fast, bounded optimization loops.
Uniform Compliance: Agents may not follow the same rules or even share the same ontology.
Immediate Deterministic Deployment & Predictable convergence via designer-set constraints, local controllers, and shared termination criteria.

How Gen-2 (MAS) differs from Gen-1 (single agent)

Gen-1 is one agent doing everything with internal subroutines.
Gen-2 is a small team of specialized agents that talk via defined messages and follow coordination rules.
The hard part moves from making a plan inside one brain to designing the interactions: who does what, who talks to whom, when, and how we check/merge the results.